Neural signals are always noisy: the
timings of when they fire, or even whether they fire at all, is
subject to random variation. We make generalizations at the
psychological level, such as saying that the speed of response is
related to intensity by a certain
formula—Pieron's Law [Hack #11].
And we also say that cells in the visual cortex respond to different
specific motions .
But both of these are true only on average. For
any single cell, or any single test of reaction time, there is
variation each time it is measured. Not all the cells in the
motion-sensitive parts of the visual cortex will respond to motion,
and those that do won't do it exactly the same each
time we experience a particular movement.

In the real world, we take averages to make sense of noisy data, and
somehow the brain must be doing this too. We know that the brain is
pretty accurate, despite the noisiness of our neural signals. A prime
mechanism for compensating for neural noise is the use of lots of
neurons so that the average response can be taken, canceling out the
noise.

But it may also be the case that noise has some useful functions in
the nervous system. Noise could be a feature, rather than just an
inconvenient bug.

In Action

A grayscale picture has noise added and the result filtered through a
threshold. The process is repeated and results played like a video.
Compare the picture with various levels of noise included. With a
small amount of noise, you see some of the gross features of the
picture—these are the parts with high light values so they
always cross the threshold, whatever the noise, and produce white
pixels—but the details don't show up often
enough for you to make them out. With lots of noise, most of the
pixels of the picture are frequently active and it's
hard to make out any distinction between true parts of the picture
and pixels randomly activated by noise.

But with the right amount of noise, you can clearly see what the
picture is and all the details. The gross features are always there
(white pixels), the fine features are there consistently enough (with
time smoothing they look gray), and the pixels that are supposed to
be black aren't activated enough to distract you.

How It Works

Having evolved to cope with noisy internal signals gives you a more
robust system. The brain has developed to handle the odd anomalous
data point, to account for random inputs thrown its way by the
environment. We can make sense of the whole even if one of the parts
doesn't entirely fit (you can see this in our
ability to simultaneously process information [Hack #52],
as well). "Happy Birthday" sung
down a crackly phone line is still "Happy
Birthday." Compare this with your precision-designed
PC; the wrong instruction at the wrong time and the whole thing
crashes. The ubiquity of noise in neural processing means your brain
is more of a statistical machine than a mechanistic one.

That's just a view of
noise as something to be worked around, however.
There's another function that noise in neural
systems might be performing—it's a phenomenon
from control theory called stochastic resonance.
This says that adding noise to a signal raises the maximum possible
combined signal level. Counterintuitively, this means that adding the
right amount of noise to a weak signal can raise it above the
threshold for detection and make it easier to detect and not less so. Figure 1 shows this in a graphical form. The
smooth curve is the varying signal, but it never quite reaches the
activation threshold. Adding noise to the signal produces the jagged
line that, although it's messy, still has the same
average values and raises it over the threshold
for detection at certain points.

Figure 1. Adding noise to a signal brings it above threshold, without changing the mean value of the signal

Just adding noise doesn't always improve things of
course: you might now have a problem with your detection threshold
being crossed even though there is no signal. A situation in which
stochastic resonance works best is one in which you have another
dimension, such as time, across which you can compare signals. Since
noise changes with time, you can make use of the frequency at which
the detection threshold is crossed too.

In
Simonotto's applet, white pixels correspond to where
the detection threshold has been crossed, and a flickering white
pixel averages to gray over time. In this example, you are using time
and space to constrain your judgment of whether you think a pixel has
been correctly activated, and you're working in
cooperation with the noise being added inside the applet, but this is
exactly what your brain can do too.

End Note

An example of a practical application of stochastic resonance theory,
in the form of a hearing aid: Morse, R. P., & Evans, E. F.
(1996). Enhancement of vowel coding for cochlear implants by addition
of noise. Nature Medicine, 2(8),
928-932.